The PAU survey

Enhancing photometric redshift estimation using DEEPz

¹ Instituto de Astronomía Teórica y Experimental (IATE), CONICET-UNC, Córdoba X5000BGR, Argentina
² Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba (UNC), Córdoba, CP X5000HUA, Argentina
³ Observatorio Astronómico de Córdoba, Universidad Nacional de Córdoba, Laprida 854, Córdoba X5000BGR, Argentina
⁴ Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain
⁵ Port d’Informació Científica (PIC), Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain
⁶ Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain
⁷ Institut d’Estudis Espacials de Catalunya (IEEC), PE-08034 Barcelona, Spain
⁸ Institute of Cosmology & Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth PO1 3FX, UK
⁹ Institute for Computational Cosmology, Department of Physics, South Road, Durham DH1 3LE, UK
¹⁰ Instituto Astrofisica de Canarias, Av. Via Lactea s/n, E38205 La Laguna, Spain
¹¹ Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing, 44780 Bochum, Germany
¹² Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Avenida Complutense 40, E-28040 Madrid, Spain
¹³ Instituto de Fisica Teorica (UAM/CSIC), Nicolas Cabrera 13, Cantoblanco, E-28049 Madrid, Spain
¹⁴ Institució Catalana de Recerca i Estudis Avançats (ICREA), 0810 Barcelona, Spain
¹⁵ Department of Astronomy, Tsinghua University, Beijing, 100084, China

^⋆ Corresponding author; vanessa.daza@unc.edu.ar

Received: 29 August 2024
Accepted: 13 November 2024

Abstract

We present photometric redshifts for 1 341 559 galaxies from the Physics of the Accelerating Universe Survey (PAUS) over 50.38 deg² of sky to i_AB = 23. Redshift estimation was performed using DEEPz, a deep learning photometric redshift code. We analysed the photometric redshift precision when varying the photometric and spectroscopic samples. Furthermore, we examined observational and instrumental effects on the precision of the photometric redshifts, and we compared photometric redshift measurements with those obtained using a template method-fitting BCNz2. Finally, we examined the use of photometric redshifts in the identification of close galaxy pairs. We find that the combination of samples from the W1 and W3 fields in the training of DEEPz significantly enhances the precision of photometric redshifts. This also occurs when we recover narrow-band fluxes using BB measurements. We show that DEEPz determines the redshifts of galaxies in the prevailing spectroscopic catalogue used in the training of DEEPz with greater precision. For the faintest galaxies (i_AB = 21 − 23), we find that DEEPz improves over BCNz2 both in terms of the precision (20–50% smaller scatter) and in returning a smaller outlier fraction in two of the wide fields. The catalogues were tested for the identification of close galaxy pairs, showing that DEEPz is effective for the identification of close galaxy pairs for samples with i_AB <  22.5 and redshift 0.2 <  z <  0.6. In addition, identifying close galaxy pairs that are common between DEEPz and BCNz2 is a promising approach for improving the accuracy of the catalogues of these systems.